Adding Noise Trajectory for Providing Privacy in Data Publishing by Vectorization

Since trajectory data is widely collected and utilized for scientific research and business purpose, publishing trajectory without proper privacy-policy leads to an acute threat to individual data. Recently, several methods, i.e., k-anonymity, l-diversity, t-closeness have been studied, though they...

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Main Authors: Tojiboev, Rashid, Lee, Wookey, Lee, Charles Cheolgi
Format: Conference Proceeding
Language:English
Subjects:
Cancer
Data privacy
Noise trajectory
Privacy
Privacy Publishing Data
Publishing
Surrogate Vector
Trajectory
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Lee, Wookey
Lee, Charles Cheolgi
description Since trajectory data is widely collected and utilized for scientific research and business purpose, publishing trajectory without proper privacy-policy leads to an acute threat to individual data. Recently, several methods, i.e., k-anonymity, l-diversity, t-closeness have been studied, though they tend to protect by reducing data depends on a feature of each method. When a strong privacy protection is required, these methods have excessively reduced data utility that may affect the result of scientific research. In this research, we suggest a novel approach to tackle this existing dilemma via an adding noise trajectory on a vector-based grid environment.
doi_str_mv 10.1109/BigComp48618.2020.00-34
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subjects Cancer
Data privacy
Noise trajectory
Privacy
Privacy Publishing Data
Publishing
Surrogate Vector
Trajectory
title Adding Noise Trajectory for Providing Privacy in Data Publishing by Vectorization
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